深度学习模型将来自公民科学和遥感数据的快速植物物种变化绘制为地图
Lauren E Gillespie1,2,3, Megan Ruffley1, Moises Exposito-Alonso1,3,4,5,6
1Department of Plant Biology, Carnegie Science, Stanford, CA 94305.
概括
一个新的深度学习模型Deepbiosphere使用遥感和公民科学数据准确地绘制了2000多种植物的分布. 该工具以高空间和时间分辨率监测生物多样性变化和息地碎片化.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 全球植物分布正在由于息地破坏和气候变化而发生转变.
- 目前的方法缺乏空间,时间和分类学分辨率,无法有效地绘制这些物种转移的地图.
研究的目的:
- 开发一个深度学习模型 (Deepbiosphere) 用于植物物种分布的高分辨率映射.
- 将遥感数据与公民科学观测相结合,以加强生物多样性监测.
主要方法:
- 通过使用来自加利福尼亚州和公民科学数据的遥感图像训练了一个深度学习模型.
- 验证了模型的性能与常见的物种分布建模方法相比.
- 在米级分辨率下应用该模型来绘制植物群落和息地碎片化的地图.
主要成果:
- 深层生物圈实现了高精度 (AUC 0.95),超过了现有方法 (AUC 0.88).
- 该模型成功地绘制了2000多种植物物种,并精确地划定了植物群落.
- 在两年内检测到严重野火对植物群体组成的快速影响.
结论:
- 将地球观测和公民科学与深度学习相结合,可以实现自动化,实时的生物多样性变化监测.
- 深层生物圈为理解和管理人类改变的景观中的生态系统提供了一个强大的工具.
- 面对全球变化,对物种分布的高分辨率绘制对于保护工作至关重要.
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